Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

We fixed Context7 biggest problem: Context bloat. Introducing the new Context7 🎉 ◆ 65% less tokens per query ◆ 38% faster queries ◆ Better output quality

Context7

7,960 subscribers

79,155 görüntüleme • 6 ay önce •via X (Twitter)

Eğitim Haberler & Politika Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

A new scoring system for Context7! 🎉 ◆ Scores code quality before feeding to LLMs ◆ Filters out low-quality snippets ◆ Better context for Cursor, Claude, etc. Quick demo 👇

A new scoring system for Context7! 🎉 ◆ Scores code quality before feeding to LLMs ◆ Filters out low-quality snippets ◆ Better context for Cursor, Claude, etc. Quick demo 👇

Context7

17,708 görüntüleme • 11 ay önce

Introducing Context7 Skills! 🎉 ◆ We extracted 24k skills from 65k repos ◆ Skills for Tailwind, React, Better-Auth, etc. ◆ Install in a single CLI command Perfect for Cursor, Claude Code & others 👇

Introducing Context7 Skills! 🎉 ◆ We extracted 24k skills from 65k repos ◆ Skills for Tailwind, React, Better-Auth, etc. ◆ Install in a single CLI command Perfect for Cursor, Claude Code & others 👇

Context7

252,165 görüntüleme • 6 ay önce

🧵 Your AI keeps forgetting. We fixed that. Introducing Neutron Personal: The universal memory layer for AI. Let us show you how we're solving the biggest problem in AI today 👇

🧵 Your AI keeps forgetting. We fixed that. Introducing Neutron Personal: The universal memory layer for AI. Let us show you how we're solving the biggest problem in AI today 👇

Vanar

89,612 görüntüleme • 11 ay önce

AI agents work better when they bring in context step by step. With Box Agent skills and progressive disclosure of LangChain skills, developers can quickly build AI agents that load the right skill at the right time, stay grounded in Box content, and keep access tightly scoped. Less context bloat. More control. Faster enterprise AI development.

AI agents work better when they bring in context step by step. With Box Agent skills and progressive disclosure of LangChain skills, developers can quickly build AI agents that load the right skill at the right time, stay grounded in Box content, and keep access tightly scoped. Less context bloat. More control. Faster enterprise AI development.

Box

627,810 görüntüleme • 3 ay önce

✨ Introducing Keypoint Action Tokens. 🤖 We translate visual observations and robot actions into a "language" that off-the-shelf LLMs can ingest and output. This transforms LLMs into *in-context, low-level imitation learning machines*. 🚀 Let me explain. 👇🧵

✨ Introducing Keypoint Action Tokens. 🤖 We translate visual observations and robot actions into a "language" that off-the-shelf LLMs can ingest and output. This transforms LLMs into in-context, low-level imitation learning machines. 🚀 Let me explain. 👇🧵

Norman Di Palo

23,097 görüntüleme • 2 yıl önce

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Google AI

424,998 görüntüleme • 6 gün önce

Introducing Eleven Multilingual v2: our new AI speech model supporting 28 languages! v2 comes with enhanced conversational capability, higher output quality & the ability to better preserve unique voice characteristics across languages. Read more:

Introducing Eleven Multilingual v2: our new AI speech model supporting 28 languages! v2 comes with enhanced conversational capability, higher output quality & the ability to better preserve unique voice characteristics across languages. Read more:

ElevenLabs

293,403 görüntüleme • 2 yıl önce

Introducing InsForge 2.0: The Backend for Agentic Development Our OSS backend provides databases, auth, storage, model gateway, and edge functions accessible through a context-optimized layer that agents can better understand and operate end-to-end. GitHub: Key Benchmarks (vs. Supabase MCP): - 14% higher accuracy - 1.3x faster per task - 2.4x fewer tokens Better. Faster. Cheaper. Build features more quickly and confidently — all at 41.7% of the cost. Shipping your ideas today. $ npx @insforge/cli create

Introducing InsForge 2.0: The Backend for Agentic Development Our OSS backend provides databases, auth, storage, model gateway, and edge functions accessible through a context-optimized layer that agents can better understand and operate end-to-end. GitHub: Key Benchmarks (vs. Supabase MCP): - 14% higher accuracy - 1.3x faster per task - 2.4x fewer tokens Better. Faster. Cheaper. Build features more quickly and confidently — all at 41.7% of the cost. Shipping your ideas today. $ npx @insforge/cli create

InsForge

1,592,190 görüntüleme • 4 ay önce

We've spent six figures in tokens testing OpenAI's 5.6 Sol model to see whether if its better than Fable and what OpenAI have done to make it even better than 5.5. 02:38 - Intro 04:10 - What Is this New Model 07:17 - First Impressions 16:38 - The Code-Quality gap 26:38 - OpenAI's Naming Problem 29:38 - The Complicated Release 32:08 - Loops & Subagents 38:28 - macOS is killing your machine 46:28 - Claude Code vs Codex Subagent UX 1:03:19 - The Pottery Analogy 1:09:33 - cispolicyd CPU Usage

We've spent six figures in tokens testing OpenAI's 5.6 Sol model to see whether if its better than Fable and what OpenAI have done to make it even better than 5.5. 02:38 - Intro 04:10 - What Is this New Model 07:17 - First Impressions 16:38 - The Code-Quality gap 26:38 - OpenAI's Naming Problem 29:38 - The Complicated Release 32:08 - Loops & Subagents 38:28 - macOS is killing your machine 46:28 - Claude Code vs Codex Subagent UX 1:03:19 - The Pottery Analogy 1:09:33 - cispolicyd CPU Usage

Nerd Snipe

84,148 görüntüleme • 18 gün önce

KIRI Engine 4.2 is here. The biggest upgrade is 3DGS to Mesh 3.0🤩 Cleaner meshes. Better thin-structure retention. Less broken geometry. 20% faster processing. We also added a new Measuring & Scaling tool for 3D printing workflows😆

KIRI Engine 4.2 is here. The biggest upgrade is 3DGS to Mesh 3.0🤩 Cleaner meshes. Better thin-structure retention. Less broken geometry. 20% faster processing. We also added a new Measuring & Scaling tool for 3D printing workflows😆

KIRI Engine - 3D Scanner App

20,203 görüntüleme • 3 ay önce

10 repos that cut your ai agent token bill by up to 80% 1. microsoft/LLMLingua → cuts prompt size by up to 95% compresses prompts before the api call. 20x compression. published at EMNLP + ACL. near-zero quality loss. 6,100 stars 2. mem0ai/mem0 → replaces full conversation history in context stores what matters. retrieves only what's needed. 10,000 token history → 200 token memory. per agent. 54,800 stars 3. BerriAI/litellm → routes each call to the cheapest model simple task → haiku. complex task → sonnet. tracks cost per agent, per call, per day. 45,700 stars 4. run-llama/llama_index → replaces sending full documents rag: 100-page doc → 3 relevant chunks → same answer. 98% fewer tokens per query. 49,100 stars 5. chroma-core/chroma → replaces keyword search in full context vector store. finds the closest match. feeds only that. 50-200 tokens per query instead of thousands. 27,800 stars 6. letta-ai/letta → replaces infinite context window crashes paged memory for agents. loads only relevant memory. stops your agent from hitting limits and retrying. 22,400 stars 7. guidance-ai/guidance → cuts output token bloat by 30-50% structured generation. constrains model output natively. no more 100-token prompts to get json back. 21,400 stars 8. Aider-AI/aider → replaces pasting entire codebases builds a repo map. sends only files relevant to the task. not your whole project. just what the agent needs. 44,300 stars 9. openai/tiktoken → count tokens before you send know the exact cost before the api call happens. not after the bill arrives. 18,100 stars 10. simonw/ttok → hard cap on what gets sent cli tool: count tokens, truncate to budget limit. pipe any text in. get truncated output back. 389 stars most agents are expensive not because the model is expensive. because nobody checked what was being sent to it.

10 repos that cut your ai agent token bill by up to 80% 1. microsoft/LLMLingua → cuts prompt size by up to 95% compresses prompts before the api call. 20x compression. published at EMNLP + ACL. near-zero quality loss. 6,100 stars 2. mem0ai/mem0 → replaces full conversation history in context stores what matters. retrieves only what's needed. 10,000 token history → 200 token memory. per agent. 54,800 stars 3. BerriAI/litellm → routes each call to the cheapest model simple task → haiku. complex task → sonnet. tracks cost per agent, per call, per day. 45,700 stars 4. run-llama/llama_index → replaces sending full documents rag: 100-page doc → 3 relevant chunks → same answer. 98% fewer tokens per query. 49,100 stars 5. chroma-core/chroma → replaces keyword search in full context vector store. finds the closest match. feeds only that. 50-200 tokens per query instead of thousands. 27,800 stars 6. letta-ai/letta → replaces infinite context window crashes paged memory for agents. loads only relevant memory. stops your agent from hitting limits and retrying. 22,400 stars 7. guidance-ai/guidance → cuts output token bloat by 30-50% structured generation. constrains model output natively. no more 100-token prompts to get json back. 21,400 stars 8. Aider-AI/aider → replaces pasting entire codebases builds a repo map. sends only files relevant to the task. not your whole project. just what the agent needs. 44,300 stars 9. openai/tiktoken → count tokens before you send know the exact cost before the api call happens. not after the bill arrives. 18,100 stars 10. simonw/ttok → hard cap on what gets sent cli tool: count tokens, truncate to budget limit. pipe any text in. get truncated output back. 389 stars most agents are expensive not because the model is expensive. because nobody checked what was being sent to it.

self.dll

39,475 görüntüleme • 2 ay önce

Introducing ERNIE X1 Turbo & ERNIE 4.5 Turbo! Building on the success of ERNIE X1 and 4.5, the upgraded ERNIE X1 Turbo and 4.5 Turbo deliver results faster and cheaper. Both models stand out for their multimodal capabilities, strong reasoning and low costs. For X1 Turbo, input and output prices start at $0.14 per 1M tokens and $0.55 per 1M tokens, respectively, priced at approximately 25% of DeepSeek R1. #BaiduCreate2025 (Exchange rate used: 1 RMB ≈ 0.14 USD)

Introducing ERNIE X1 Turbo & ERNIE 4.5 Turbo! Building on the success of ERNIE X1 and 4.5, the upgraded ERNIE X1 Turbo and 4.5 Turbo deliver results faster and cheaper. Both models stand out for their multimodal capabilities, strong reasoning and low costs. For X1 Turbo, input and output prices start at $0.14 per 1M tokens and $0.55 per 1M tokens, respectively, priced at approximately 25% of DeepSeek R1. #BaiduCreate2025 (Exchange rate used: 1 RMB ≈ 0.14 USD)

Baidu Inc.

136,280 görüntüleme • 1 yıl önce

SSGI has received a lot of updates like better denoising, improved temporal reprojection, less smearing. Also fixed some device issues. The new version is up to 90% faster than the last version. 🎊 Test it: Still some work and bug fixing to do...

SSGI has received a lot of updates like better denoising, improved temporal reprojection, less smearing. Also fixed some device issues. The new version is up to 90% faster than the last version. 🎊 Test it: Still some work and bug fixing to do...

0beqz

17,324 görüntüleme • 2 yıl önce

Introducing: LetzAI V4. We're excited to release the newest version of LetzAI with Video Models, a new Image Editor, a new Upscaler, an improved LetzAI Chat, and a better UI. All at $0.01 per high-quality image. Create what you love.

Introducing: LetzAI V4. We're excited to release the newest version of LetzAI with Video Models, a new Image Editor, a new Upscaler, an improved LetzAI Chat, and a better UI. All at $0.01 per high-quality image. Create what you love.

LetzAI

112,913 görüntüleme • 10 ay önce

New: America Fun 🇺🇸 Moby just became the first mobile app to natively identify coins launched on America.Fun 🇺🇸🦅. This means faster discovery, clearer context, and less noise when new tokens hit the market. Look for the 🇺🇸 icon — or instantly surface trending coins using the Launchpad filter. More signal. Earlier discovery. One more edge, built straight into Moby.

New: America Fun 🇺🇸 Moby just became the first mobile app to natively identify coins launched on America.Fun 🇺🇸🦅. This means faster discovery, clearer context, and less noise when new tokens hit the market. Look for the 🇺🇸 icon — or instantly surface trending coins using the Launchpad filter. More signal. Earlier discovery. One more edge, built straight into Moby.

Moby

14,688 görüntüleme • 6 ay önce

BILL GATES: THE FASTER WE IMPROVE HEALTH, THE FASTER FAMILY SIZE GOES DOWN “Melinda and I wondered whether providing new medicines and keeping children alive would create more of a population problem. What we found out is that as health improves, families choose to have less children, and this effect is very, very dramatic. The faster we improve health, the faster family size goes down, and so, we can feel great about saving those lives.” Source: Bill Gates

BILL GATES: THE FASTER WE IMPROVE HEALTH, THE FASTER FAMILY SIZE GOES DOWN “Melinda and I wondered whether providing new medicines and keeping children alive would create more of a population problem. What we found out is that as health improves, families choose to have less children, and this effect is very, very dramatic. The faster we improve health, the faster family size goes down, and so, we can feel great about saving those lives.” Source: Bill Gates

Mario Nawfal

656,674 görüntüleme • 1 yıl önce

$Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.$

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,198,960 görüntüleme • 2 ay önce

Ethereum's Fusaka Upgrade Explained The next major Ethereum hard fork just hit its final testnet this week and it’s the biggest since The Merge. Fusaka unlocks: ➡️ PeerDAS → faster L2 data processing ➡️ Verkle Trees → stateless clients & lighter nodes ➡️ Higher gas limit → more complex dApps ➡️ Faster sync → less bloat This upgrade isn’t just technical, it changes how we build. Shoutout to Ethereum Foundation, yinka for making this video possible!

Ethereum's Fusaka Upgrade Explained The next major Ethereum hard fork just hit its final testnet this week and it’s the biggest since The Merge. Fusaka unlocks: ➡️ PeerDAS → faster L2 data processing ➡️ Verkle Trees → stateless clients & lighter nodes ➡️ Higher gas limit → more complex dApps ➡️ Faster sync → less bloat This upgrade isn’t just technical, it changes how we build. Shoutout to Ethereum Foundation, yinka for making this video possible!

Francesco Andreoli ᵍᵐ

14,826 görüntüleme • 9 ay önce

OpenHands crushed Codex by 2.3× on token efficiency! We gave both agents the same task on the same model (Qwen3.5 35B): build an 8-bit Space Invaders in 3 iterations (build, fix, polish). Output: • OpenHands: 219K tokens, • Codex: 513K tokens, OpenHands beats Codex in local running. The difference is in how they handle context. OpenHands reuses the unchanged data across every pass and only pays for new tokens, while Codex re-sends and re-counts it every iteration. OpenHands finished a bit slower, but on local runs, tokens spent are the real cost.

OpenHands crushed Codex by 2.3× on token efficiency! We gave both agents the same task on the same model (Qwen3.5 35B): build an 8-bit Space Invaders in 3 iterations (build, fix, polish). Output: • OpenHands: 219K tokens, • Codex: 513K tokens, OpenHands beats Codex in local running. The difference is in how they handle context. OpenHands reuses the unchanged data across every pass and only pays for new tokens, while Codex re-sends and re-counts it every iteration. OpenHands finished a bit slower, but on local runs, tokens spent are the real cost.

atomic.chat

28,088 görüntüleme • 10 gün önce

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

226,054 görüntüleme • 4 ay önce